Overview

Brought to you by YData

Dataset statistics

Number of variables30
Number of observations700
Missing cells2054
Missing cells (%)9.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory197.3 KiB
Average record size in memory288.6 B

Variable types

Categorical9
Unsupported1
Numeric15
Text3
DateTime2

Alerts

cve_ent has constant value "26"Constant
cve_mun has constant value "30"Constant
cve_loc has constant value "1"Constant
nom_loc has constant value "Hermosillo"Constant
incidentes_fin_semana has constant value "0"Constant
incidentes_quincena has constant value "0"Constant
CVE_COL has a high cardinality: 700 distinct valuesHigh cardinality
COLONIA has a high cardinality: 700 distinct valuesHigh cardinality
CP has a high cardinality: 96 distinct valuesHigh cardinality
CP is highly overall correlated with tasa_alta_severidad_per_1k and 1 other fieldsHigh correlation
area_km2 is highly overall correlated with incidentes_alta and 5 other fieldsHigh correlation
incidentes_alta is highly overall correlated with area_km2 and 8 other fieldsHigh correlation
incidentes_baja is highly overall correlated with area_km2 and 8 other fieldsHigh correlation
incidentes_media is highly overall correlated with area_km2 and 8 other fieldsHigh correlation
pctj_hombres is highly overall correlated with pctj_mujeresHigh correlation
pctj_mujeres is highly overall correlated with pctj_hombresHigh correlation
poblacion_total is highly overall correlated with area_km2 and 5 other fieldsHigh correlation
score_severidad is highly overall correlated with incidentes_alta and 5 other fieldsHigh correlation
tasa_alta_severidad_per_1k is highly overall correlated with CP and 6 other fieldsHigh correlation
tasa_incidentes_per_1k is highly overall correlated with CP and 6 other fieldsHigh correlation
total_incidentes is highly overall correlated with area_km2 and 8 other fieldsHigh correlation
viviendas_totales is highly overall correlated with area_km2 and 5 other fieldsHigh correlation
otros_cp has 700 (100.0%) missing valuesMissing
categorias_dict has 175 (25.0%) missing valuesMissing
partes_dia_dict has 175 (25.0%) missing valuesMissing
dias_semana_dict has 175 (25.0%) missing valuesMissing
fecha_inicio has 175 (25.0%) missing valuesMissing
fecha_fin has 175 (25.0%) missing valuesMissing
poblacion_total has 41 (5.9%) missing valuesMissing
viviendas_totales has 41 (5.9%) missing valuesMissing
escolaridad_años_prom has 58 (8.3%) missing valuesMissing
pctj_menores18 has 58 (8.3%) missing valuesMissing
pctj_hombres has 58 (8.3%) missing valuesMissing
pctj_mujeres has 58 (8.3%) missing valuesMissing
tasa_incidentes_per_1k has 55 (7.9%) missing valuesMissing
tasa_alta_severidad_per_1k has 55 (7.9%) missing valuesMissing
densidad_poblacional has 55 (7.9%) missing valuesMissing
tasa_incidentes_per_1k is highly skewed (γ1 = 25.29877513)Skewed
tasa_alta_severidad_per_1k is highly skewed (γ1 = 25.32487058)Skewed
CVE_COL is uniformly distributedUniform
COLONIA is uniformly distributedUniform
CVE_COL has unique valuesUnique
COLONIA has unique valuesUnique
area_km2 has unique valuesUnique
otros_cp is an unsupported type, check if it needs cleaning or further analysisUnsupported
total_incidentes has 175 (25.0%) zerosZeros
incidentes_alta has 191 (27.3%) zerosZeros
incidentes_media has 194 (27.7%) zerosZeros
incidentes_baja has 202 (28.9%) zerosZeros
poblacion_total has 14 (2.0%) zerosZeros
viviendas_totales has 8 (1.1%) zerosZeros
tasa_incidentes_per_1k has 140 (20.0%) zerosZeros
tasa_alta_severidad_per_1k has 156 (22.3%) zerosZeros
score_severidad has 175 (25.0%) zerosZeros

Reproduction

Analysis started2025-11-11 05:41:48.515371
Analysis finished2025-11-11 05:42:43.055674
Duration54.54 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

cve_ent
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
26
700 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1400
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26
2nd row26
3rd row26
4th row26
5th row26

Common Values

ValueCountFrequency (%)
26700
100.0%

Length

2025-11-10T22:42:43.265474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-10T22:42:43.515251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
26700
100.0%

Most occurring characters

ValueCountFrequency (%)
2700
50.0%
6700
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2700
50.0%
6700
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2700
50.0%
6700
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2700
50.0%
6700
50.0%

cve_mun
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
30
700 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1400
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30
2nd row30
3rd row30
4th row30
5th row30

Common Values

ValueCountFrequency (%)
30700
100.0%

Length

2025-11-10T22:42:43.771621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-10T22:42:43.997805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
30700
100.0%

Most occurring characters

ValueCountFrequency (%)
3700
50.0%
0700
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3700
50.0%
0700
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3700
50.0%
0700
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3700
50.0%
0700
50.0%

cve_loc
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1
700 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1700
100.0%

Length

2025-11-10T22:42:44.215374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-10T22:42:44.420701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1700
100.0%

Most occurring characters

ValueCountFrequency (%)
1700
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1700
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1700
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1700
100.0%

nom_loc
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Hermosillo
700 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters7000
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHermosillo
2nd rowHermosillo
3rd rowHermosillo
4th rowHermosillo
5th rowHermosillo

Common Values

ValueCountFrequency (%)
Hermosillo700
100.0%

Length

2025-11-10T22:42:44.657691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-10T22:42:44.833128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
hermosillo700
100.0%

Most occurring characters

ValueCountFrequency (%)
o1400
20.0%
l1400
20.0%
H700
10.0%
e700
10.0%
r700
10.0%
m700
10.0%
s700
10.0%
i700
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)7000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o1400
20.0%
l1400
20.0%
H700
10.0%
e700
10.0%
r700
10.0%
m700
10.0%
s700
10.0%
i700
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o1400
20.0%
l1400
20.0%
H700
10.0%
e700
10.0%
r700
10.0%
m700
10.0%
s700
10.0%
i700
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o1400
20.0%
l1400
20.0%
H700
10.0%
e700
10.0%
r700
10.0%
m700
10.0%
s700
10.0%
i700
10.0%

CVE_COL
Categorical

High cardinality  Uniform  Unique 

Distinct700
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
2603000010
 
1
2603000012156
 
1
2603000012052
 
1
2603000012064
 
1
2603000012078
 
1
Other values (695)
695 

Length

Max length13
Median length13
Mean length12.995714
Min length10

Characters and Unicode

Total characters9097
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique700 ?
Unique (%)100.0%

Sample

1st row2603000016735
2nd row2603000011785
3rd row2603000016335
4th row2603000011480
5th row2603000011663

Common Values

ValueCountFrequency (%)
26030000101
 
0.1%
26030000121561
 
0.1%
26030000120521
 
0.1%
26030000120641
 
0.1%
26030000120781
 
0.1%
26030000120981
 
0.1%
26030000120991
 
0.1%
26030000121101
 
0.1%
26030000121131
 
0.1%
26030000121551
 
0.1%
Other values (690)690
98.6%

Length

2025-11-10T22:42:45.054842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
26030000101
 
0.1%
26030000113531
 
0.1%
26030000113541
 
0.1%
26030000113461
 
0.1%
26030000113471
 
0.1%
26030000113481
 
0.1%
26030000113491
 
0.1%
26030000113501
 
0.1%
26030000113511
 
0.1%
26030000113521
 
0.1%
Other values (690)690
98.6%

Most occurring characters

ValueCountFrequency (%)
03643
40.0%
11301
 
14.3%
61143
 
12.6%
2947
 
10.4%
3922
 
10.1%
4279
 
3.1%
5256
 
2.8%
7241
 
2.6%
8212
 
2.3%
9153
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)9097
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03643
40.0%
11301
 
14.3%
61143
 
12.6%
2947
 
10.4%
3922
 
10.1%
4279
 
3.1%
5256
 
2.8%
7241
 
2.6%
8212
 
2.3%
9153
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9097
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03643
40.0%
11301
 
14.3%
61143
 
12.6%
2947
 
10.4%
3922
 
10.1%
4279
 
3.1%
5256
 
2.8%
7241
 
2.6%
8212
 
2.3%
9153
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9097
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03643
40.0%
11301
 
14.3%
61143
 
12.6%
2947
 
10.4%
3922
 
10.1%
4279
 
3.1%
5256
 
2.8%
7241
 
2.6%
8212
 
2.3%
9153
 
1.7%

COLONIA
Categorical

High cardinality  Uniform  Unique 

Distinct700
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
14 DE MARZO
 
1
PUERTA DEL REY SECCIN GALICIA
 
1
PUEBLO ALEGRE
 
1
PUEBLO ALTO
 
1
PUEBLO BONITO
 
1
Other values (695)
695 

Length

Max length39
Median length29
Mean length15.754286
Min length4

Characters and Unicode

Total characters11028
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique700 ?
Unique (%)100.0%

Sample

1st rowALTA FIRENZE NORTE RESIDENCIAL
2nd rowJORGE VALDEZ MUÑOZ
3rd rowVILLA VERDE CERRADA SAN VICENTE
4th rowVILLA VENTURA
5th rowNUEVO HERMOSILLO

Common Values

ValueCountFrequency (%)
14 DE MARZO1
 
0.1%
PUERTA DEL REY SECCIN GALICIA1
 
0.1%
PUEBLO ALEGRE1
 
0.1%
PUEBLO ALTO1
 
0.1%
PUEBLO BONITO1
 
0.1%
PUEBLO DEL ORO1
 
0.1%
PUEBLO DEL SOL1
 
0.1%
PUEBLO ESCONDIDO1
 
0.1%
PUERTA DE HIERRO1
 
0.1%
PUERTA DEL REY ETAPA III1
 
0.1%
Other values (690)690
98.6%

Length

2025-11-10T22:42:45.351895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
residencial101
 
5.8%
del82
 
4.7%
de64
 
3.7%
la46
 
2.7%
san44
 
2.5%
villa43
 
2.5%
los36
 
2.1%
las35
 
2.0%
real29
 
1.7%
cerrada28
 
1.6%
Other values (630)1220
70.6%

Most occurring characters

ValueCountFrequency (%)
A1522
13.8%
E1086
9.8%
1030
9.3%
L926
8.4%
I830
 
7.5%
R816
 
7.4%
S755
 
6.8%
O728
 
6.6%
N627
 
5.7%
C523
 
4.7%
Other values (30)2185
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)11028
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A1522
13.8%
E1086
9.8%
1030
9.3%
L926
8.4%
I830
 
7.5%
R816
 
7.4%
S755
 
6.8%
O728
 
6.6%
N627
 
5.7%
C523
 
4.7%
Other values (30)2185
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11028
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A1522
13.8%
E1086
9.8%
1030
9.3%
L926
8.4%
I830
 
7.5%
R816
 
7.4%
S755
 
6.8%
O728
 
6.6%
N627
 
5.7%
C523
 
4.7%
Other values (30)2185
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11028
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A1522
13.8%
E1086
9.8%
1030
9.3%
L926
8.4%
I830
 
7.5%
R816
 
7.4%
S755
 
6.8%
O728
 
6.6%
N627
 
5.7%
C523
 
4.7%
Other values (30)2185
19.8%

CP
Categorical

High cardinality  High correlation 

Distinct96
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
83170.0
67 
83240.0
52 
83104.0
 
45
83105.0
 
38
83118.0
 
37
Other values (91)
461 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters4900
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)4.6%

Sample

1st row83104.0
2nd row83104.0
3rd row83118.0
4th row83159.0
5th row83296.0

Common Values

ValueCountFrequency (%)
83170.067
 
9.6%
83240.052
 
7.4%
83104.045
 
6.4%
83105.038
 
5.4%
83118.037
 
5.3%
83106.033
 
4.7%
83288.033
 
4.7%
83287.027
 
3.9%
83140.026
 
3.7%
83290.022
 
3.1%
Other values (86)320
45.7%

Length

2025-11-10T22:42:45.644240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
83170.067
 
9.6%
83240.052
 
7.4%
83104.045
 
6.4%
83105.038
 
5.4%
83118.037
 
5.3%
83106.033
 
4.7%
83288.033
 
4.7%
83287.027
 
3.9%
83140.026
 
3.7%
83290.022
 
3.1%
Other values (86)320
45.7%

Most occurring characters

ValueCountFrequency (%)
01160
23.7%
8889
18.1%
3724
14.8%
.700
14.3%
1499
10.2%
2277
 
5.7%
4205
 
4.2%
7154
 
3.1%
5100
 
2.0%
698
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)4900
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01160
23.7%
8889
18.1%
3724
14.8%
.700
14.3%
1499
10.2%
2277
 
5.7%
4205
 
4.2%
7154
 
3.1%
5100
 
2.0%
698
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4900
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01160
23.7%
8889
18.1%
3724
14.8%
.700
14.3%
1499
10.2%
2277
 
5.7%
4205
 
4.2%
7154
 
3.1%
5100
 
2.0%
698
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4900
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01160
23.7%
8889
18.1%
3724
14.8%
.700
14.3%
1499
10.2%
2277
 
5.7%
4205
 
4.2%
7154
 
3.1%
5100
 
2.0%
698
 
2.0%

otros_cp
Unsupported

Missing  Rejected  Unsupported 

Missing700
Missing (%)100.0%
Memory size5.6 KiB

total_incidentes
Real number (ℝ)

High correlation  Zeros 

Distinct450
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3109.6457
Minimum0
Maximum157969
Zeros175
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-11-10T22:42:45.900416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median430.5
Q32675
95-th percentile14718.7
Maximum157969
Range157969
Interquartile range (IQR)2674.25

Descriptive statistics

Standard deviation8735.782
Coefficient of variation (CV)2.8092531
Kurtosis147.69588
Mean3109.6457
Median Absolute Deviation (MAD)430.5
Skewness9.7381103
Sum2176752
Variance76313886
MonotonicityNot monotonic
2025-11-10T22:42:46.236081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0175
 
25.0%
123
 
3.3%
46
 
0.9%
26
 
0.9%
165
 
0.7%
55
 
0.7%
1073
 
0.4%
2313
 
0.4%
33
 
0.4%
1602
 
0.3%
Other values (440)469
67.0%
ValueCountFrequency (%)
0175
25.0%
123
 
3.3%
26
 
0.9%
33
 
0.4%
46
 
0.9%
55
 
0.7%
62
 
0.3%
81
 
0.1%
91
 
0.1%
102
 
0.3%
ValueCountFrequency (%)
1579691
0.1%
581911
0.1%
547571
0.1%
509821
0.1%
437611
0.1%
368431
0.1%
361781
0.1%
357541
0.1%
354521
0.1%
342161
0.1%

incidentes_alta
Real number (ℝ)

High correlation  Zeros 

Distinct400
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1087.2129
Minimum0
Maximum46261
Zeros191
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-11-10T22:42:46.505318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median134
Q3865
95-th percentile5654.65
Maximum46261
Range46261
Interquartile range (IQR)865

Descriptive statistics

Standard deviation2872.708
Coefficient of variation (CV)2.6422683
Kurtosis94.722337
Mean1087.2129
Median Absolute Deviation (MAD)134
Skewness7.6200702
Sum761049
Variance8252451.4
MonotonicityNot monotonic
2025-11-10T22:42:46.807864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0191
27.3%
117
 
2.4%
210
 
1.4%
36
 
0.9%
56
 
0.9%
75
 
0.7%
814
 
0.6%
144
 
0.6%
704
 
0.6%
234
 
0.6%
Other values (390)449
64.1%
ValueCountFrequency (%)
0191
27.3%
117
 
2.4%
210
 
1.4%
36
 
0.9%
43
 
0.4%
56
 
0.9%
61
 
0.1%
75
 
0.7%
81
 
0.1%
91
 
0.1%
ValueCountFrequency (%)
462611
0.1%
195351
0.1%
194071
0.1%
154151
0.1%
149711
0.1%
129681
0.1%
122551
0.1%
116911
0.1%
116121
0.1%
114531
0.1%

incidentes_media
Real number (ℝ)

High correlation  Zeros 

Distinct420
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1256.2
Minimum0
Maximum70784
Zeros194
Zeros (%)27.7%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-11-10T22:42:47.017732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median163.5
Q31109.75
95-th percentile5905.95
Maximum70784
Range70784
Interquartile range (IQR)1109.75

Descriptive statistics

Standard deviation3780.4244
Coefficient of variation (CV)3.0094128
Kurtosis172.78522
Mean1256.2
Median Absolute Deviation (MAD)163.5
Skewness10.815955
Sum879340
Variance14291608
MonotonicityNot monotonic
2025-11-10T22:42:47.239785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0194
27.7%
122
 
3.1%
45
 
0.7%
554
 
0.6%
164
 
0.6%
594
 
0.6%
244
 
0.6%
84
 
0.6%
323
 
0.4%
813
 
0.4%
Other values (410)453
64.7%
ValueCountFrequency (%)
0194
27.7%
122
 
3.1%
23
 
0.4%
31
 
0.1%
45
 
0.7%
51
 
0.1%
62
 
0.3%
73
 
0.4%
84
 
0.6%
102
 
0.3%
ValueCountFrequency (%)
707841
0.1%
312171
0.1%
236801
0.1%
208531
0.1%
163731
0.1%
162031
0.1%
161051
0.1%
149911
0.1%
140371
0.1%
139241
0.1%

incidentes_baja
Real number (ℝ)

High correlation  Zeros 

Distinct379
Distinct (%)54.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean766.23286
Minimum0
Maximum40924
Zeros202
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-11-10T22:42:47.504675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median98
Q3691.25
95-th percentile3389.35
Maximum40924
Range40924
Interquartile range (IQR)691.25

Descriptive statistics

Standard deviation2183.4285
Coefficient of variation (CV)2.8495626
Kurtosis168.94036
Mean766.23286
Median Absolute Deviation (MAD)98
Skewness10.471556
Sum536363
Variance4767360.1
MonotonicityNot monotonic
2025-11-10T22:42:47.738289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0202
28.9%
118
 
2.6%
28
 
1.1%
36
 
0.9%
115
 
0.7%
1184
 
0.6%
204
 
0.6%
334
 
0.6%
424
 
0.6%
44
 
0.6%
Other values (369)441
63.0%
ValueCountFrequency (%)
0202
28.9%
118
 
2.6%
28
 
1.1%
36
 
0.9%
44
 
0.6%
53
 
0.4%
63
 
0.4%
82
 
0.3%
91
 
0.1%
103
 
0.4%
ValueCountFrequency (%)
409241
0.1%
144221
0.1%
120031
0.1%
116701
0.1%
105941
0.1%
95791
0.1%
90721
0.1%
84831
0.1%
84451
0.1%
81931
0.1%

categorias_dict
Text

Missing 

Distinct507
Distinct (%)96.6%
Missing175
Missing (%)25.0%
Memory size5.6 KiB
2025-11-10T22:42:48.789598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length299
Median length277
Mean length235.04952
Min length14

Characters and Unicode

Total characters123401
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique503 ?
Unique (%)95.8%

Sample

1st row{'DELITO PATRIMONIAL': 1}
2nd row{'CONVIVENCIA': 1009, 'VIOLENCIA': 799, 'EMERGENCIAS MÉDICAS': 406, 'DELITO PATRIMONIAL': 244, 'INCENDIOS Y EXPLOSIONES': 204, 'RESCATE': 125, 'TRÁNSITO': 108, 'INFRAESTRUCTURA': 53, 'OTROS ACTOS LEGALES': 50, 'DELITO CONTRA SALUD': 46, 'DESASTRES NATURALES': 19, 'ELECTORAL': 3}
3rd row{'CONVIVENCIA': 18291, 'VIOLENCIA': 10773, 'DELITO PATRIMONIAL': 3245, 'EMERGENCIAS MÉDICAS': 2950, 'TRÁNSITO': 2711, 'INCENDIOS Y EXPLOSIONES': 1789, 'RESCATE': 1476, 'INFRAESTRUCTURA': 718, 'OTROS ACTOS LEGALES': 692, 'DELITO CONTRA SALUD': 555, 'DESASTRES NATURALES': 538, 'ELECTORAL': 23}
4th row{'DELITO PATRIMONIAL': 1}
5th row{'CONVIVENCIA': 5307, 'VIOLENCIA': 3892, 'TRÁNSITO': 3707, 'DELITO PATRIMONIAL': 1557, 'EMERGENCIAS MÉDICAS': 1530, 'RESCATE': 1403, 'DESASTRES NATURALES': 1046, 'INCENDIOS Y EXPLOSIONES': 644, 'INFRAESTRUCTURA': 542, 'OTROS ACTOS LEGALES': 448, 'DELITO CONTRA SALUD': 183, 'ELECTORAL': 1}
ValueCountFrequency (%)
delito908
 
6.2%
violencia502
 
3.4%
convivencia502
 
3.4%
emergencias483
 
3.3%
médicas483
 
3.3%
patrimonial482
 
3.3%
tránsito480
 
3.3%
rescate473
 
3.2%
explosiones470
 
3.2%
y470
 
3.2%
Other values (1203)9482
64.4%
2025-11-10T22:42:50.191851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14210
 
11.5%
'10640
 
8.6%
E8689
 
7.0%
A7557
 
6.1%
S7327
 
5.9%
I7206
 
5.8%
O6204
 
5.0%
T6109
 
5.0%
N5695
 
4.6%
:5320
 
4.3%
Other values (27)44444
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)123401
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14210
 
11.5%
'10640
 
8.6%
E8689
 
7.0%
A7557
 
6.1%
S7327
 
5.9%
I7206
 
5.8%
O6204
 
5.0%
T6109
 
5.0%
N5695
 
4.6%
:5320
 
4.3%
Other values (27)44444
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)123401
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14210
 
11.5%
'10640
 
8.6%
E8689
 
7.0%
A7557
 
6.1%
S7327
 
5.9%
I7206
 
5.8%
O6204
 
5.0%
T6109
 
5.0%
N5695
 
4.6%
:5320
 
4.3%
Other values (27)44444
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)123401
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14210
 
11.5%
'10640
 
8.6%
E8689
 
7.0%
A7557
 
6.1%
S7327
 
5.9%
I7206
 
5.8%
O6204
 
5.0%
T6109
 
5.0%
N5695
 
4.6%
:5320
 
4.3%
Other values (27)44444
36.0%

partes_dia_dict
Text

Missing 

Distinct505
Distinct (%)96.2%
Missing175
Missing (%)25.0%
Memory size5.6 KiB
2025-11-10T22:42:51.149330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length69
Median length67
Mean length57.340952
Min length12

Characters and Unicode

Total characters30104
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)95.2%

Sample

1st row{'Tarde': 1}
2nd row{'Noche': 989, 'Madrugada': 843, 'Tarde': 713, 'Mañana': 521}
3rd row{'Noche': 14404, 'Madrugada': 13146, 'Tarde': 9230, 'Mañana': 6981}
4th row{'Mañana': 1}
5th row{'Tarde': 5966, 'Noche': 5718, 'Mañana': 5244, 'Madrugada': 3332}
ValueCountFrequency (%)
tarde501
 
12.6%
mañana498
 
12.5%
madrugada495
 
12.4%
noche495
 
12.4%
154
 
1.4%
237
 
0.9%
418
 
0.5%
1515
 
0.4%
813
 
0.3%
313
 
0.3%
Other values (1079)1839
46.2%
2025-11-10T22:42:52.302627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
'3978
 
13.2%
a3480
 
11.6%
3453
 
11.5%
:1989
 
6.6%
d1491
 
5.0%
,1464
 
4.9%
11018
 
3.4%
e996
 
3.3%
r996
 
3.3%
M993
 
3.3%
Other values (20)10246
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)30104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
'3978
 
13.2%
a3480
 
11.6%
3453
 
11.5%
:1989
 
6.6%
d1491
 
5.0%
,1464
 
4.9%
11018
 
3.4%
e996
 
3.3%
r996
 
3.3%
M993
 
3.3%
Other values (20)10246
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)30104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
'3978
 
13.2%
a3480
 
11.6%
3453
 
11.5%
:1989
 
6.6%
d1491
 
5.0%
,1464
 
4.9%
11018
 
3.4%
e996
 
3.3%
r996
 
3.3%
M993
 
3.3%
Other values (20)10246
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)30104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
'3978
 
13.2%
a3480
 
11.6%
3453
 
11.5%
:1989
 
6.6%
d1491
 
5.0%
,1464
 
4.9%
11018
 
3.4%
e996
 
3.3%
r996
 
3.3%
M993
 
3.3%
Other values (20)10246
34.0%

incidentes_fin_semana
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0
700 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0700
100.0%

Length

2025-11-10T22:42:52.543999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-10T22:42:52.752081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0700
100.0%

Most occurring characters

ValueCountFrequency (%)
0700
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0700
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0700
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0700
100.0%

incidentes_quincena
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0
700 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0700
100.0%

Length

2025-11-10T22:42:53.005795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-10T22:42:53.281610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0700
100.0%

Most occurring characters

ValueCountFrequency (%)
0700
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0700
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0700
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0700
100.0%

dias_semana_dict
Text

Missing 

Distinct506
Distinct (%)96.4%
Missing175
Missing (%)25.0%
Memory size5.6 KiB
2025-11-10T22:42:54.361836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length123
Median length116
Mean length99.653333
Min length12

Characters and Unicode

Total characters52318
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique498 ?
Unique (%)94.9%

Sample

1st row{'Martes': 1}
2nd row{'Domingo': 750, 'Sábado': 472, 'Lunes': 463, 'Viernes': 371, 'Martes': 348, 'Miércoles': 337, 'Jueves': 325}
3rd row{'Domingo': 10522, 'Sábado': 7056, 'Lunes': 6029, 'Viernes': 5295, 'Jueves': 5050, 'Miércoles': 4966, 'Martes': 4843}
4th row{'Martes': 1}
5th row{'Viernes': 3005, 'Jueves': 2986, 'Lunes': 2968, 'Miércoles': 2893, 'Martes': 2890, 'Domingo': 2853, 'Sábado': 2665}
ValueCountFrequency (%)
domingo497
 
7.2%
martes491
 
7.2%
sábado490
 
7.1%
lunes489
 
7.1%
viernes488
 
7.1%
miércoles487
 
7.1%
jueves486
 
7.1%
1104
 
1.5%
249
 
0.7%
337
 
0.5%
Other values (1267)3238
47.2%
2025-11-10T22:42:55.806057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
'6856
 
13.1%
6331
 
12.1%
:3428
 
6.6%
e3415
 
6.5%
,2903
 
5.5%
s2441
 
4.7%
o1971
 
3.8%
11740
 
3.3%
n1474
 
2.8%
i1472
 
2.8%
Other values (30)20287
38.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)52318
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
'6856
 
13.1%
6331
 
12.1%
:3428
 
6.6%
e3415
 
6.5%
,2903
 
5.5%
s2441
 
4.7%
o1971
 
3.8%
11740
 
3.3%
n1474
 
2.8%
i1472
 
2.8%
Other values (30)20287
38.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)52318
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
'6856
 
13.1%
6331
 
12.1%
:3428
 
6.6%
e3415
 
6.5%
,2903
 
5.5%
s2441
 
4.7%
o1971
 
3.8%
11740
 
3.3%
n1474
 
2.8%
i1472
 
2.8%
Other values (30)20287
38.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)52318
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
'6856
 
13.1%
6331
 
12.1%
:3428
 
6.6%
e3415
 
6.5%
,2903
 
5.5%
s2441
 
4.7%
o1971
 
3.8%
11740
 
3.3%
n1474
 
2.8%
i1472
 
2.8%
Other values (30)20287
38.8%

fecha_inicio
Date

Missing 

Distinct251
Distinct (%)47.8%
Missing175
Missing (%)25.0%
Memory size5.6 KiB
Minimum2018-01-01 00:00:00
Maximum2025-05-13 07:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-10T22:42:56.056333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:42:56.359291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

fecha_fin
Date

Missing 

Distinct275
Distinct (%)52.4%
Missing175
Missing (%)25.0%
Memory size5.6 KiB
Minimum2018-01-01 00:00:00
Maximum2025-09-30 23:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-10T22:42:56.573409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:42:56.876810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

poblacion_total
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct553
Distinct (%)83.9%
Missing41
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean1290
Minimum0
Maximum20398
Zeros14
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-11-10T22:42:57.121543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.9
Q1260.5
median618
Q31390.5
95-th percentile4761.4
Maximum20398
Range20398
Interquartile range (IQR)1130

Descriptive statistics

Standard deviation2095.3216
Coefficient of variation (CV)1.6242803
Kurtosis27.938063
Mean1290
Median Absolute Deviation (MAD)465
Skewness4.4684147
Sum850110
Variance4390372.6
MonotonicityNot monotonic
2025-11-10T22:42:57.385865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014
 
2.0%
2013
 
0.4%
103
 
0.4%
443
 
0.4%
783
 
0.4%
2693
 
0.4%
2093
 
0.4%
3383
 
0.4%
3513
 
0.4%
2903
 
0.4%
Other values (543)618
88.3%
(Missing)41
 
5.9%
ValueCountFrequency (%)
014
2.0%
62
 
0.3%
81
 
0.1%
103
 
0.4%
142
 
0.3%
151
 
0.1%
171
 
0.1%
191
 
0.1%
201
 
0.1%
211
 
0.1%
ValueCountFrequency (%)
203981
0.1%
197481
0.1%
162831
0.1%
126291
0.1%
120551
0.1%
113701
0.1%
110021
0.1%
100081
0.1%
99371
0.1%
97611
0.1%

viviendas_totales
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct442
Distinct (%)67.1%
Missing41
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean445.3308
Minimum0
Maximum7175
Zeros8
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-11-10T22:42:57.631513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q192.5
median216
Q3491
95-th percentile1666.5
Maximum7175
Range7175
Interquartile range (IQR)398.5

Descriptive statistics

Standard deviation715.97892
Coefficient of variation (CV)1.6077462
Kurtosis26.997999
Mean445.3308
Median Absolute Deviation (MAD)153
Skewness4.4151072
Sum293473
Variance512625.81
MonotonicityNot monotonic
2025-11-10T22:42:57.924246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08
 
1.1%
895
 
0.7%
2825
 
0.7%
1254
 
0.6%
214
 
0.6%
304
 
0.6%
514
 
0.6%
134
 
0.6%
34
 
0.6%
2294
 
0.6%
Other values (432)613
87.6%
(Missing)41
 
5.9%
ValueCountFrequency (%)
08
1.1%
13
 
0.4%
34
0.6%
42
 
0.3%
71
 
0.1%
91
 
0.1%
101
 
0.1%
112
 
0.3%
134
0.6%
144
0.6%
ValueCountFrequency (%)
71751
0.1%
65511
0.1%
47451
0.1%
41021
0.1%
39251
0.1%
39161
0.1%
38631
0.1%
38451
0.1%
36351
0.1%
36182
0.3%

escolaridad_años_prom
Real number (ℝ)

Missing 

Distinct88
Distinct (%)13.7%
Missing58
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean12.433022
Minimum1.7
Maximum18.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-11-10T22:42:58.187499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.7
5-th percentile9.105
Q110.5
median12.7
Q314.5
95-th percentile15.3
Maximum18.7
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2507547
Coefficient of variation (CV)0.18103039
Kurtosis-0.06194767
Mean12.433022
Median Absolute Deviation (MAD)2
Skewness-0.41296924
Sum7982
Variance5.0658969
MonotonicityNot monotonic
2025-11-10T22:42:58.450158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.920
 
2.9%
14.720
 
2.9%
15.117
 
2.4%
12.715
 
2.1%
14.315
 
2.1%
9.715
 
2.1%
10.614
 
2.0%
14.514
 
2.0%
14.614
 
2.0%
15.314
 
2.0%
Other values (78)484
69.1%
(Missing)58
 
8.3%
ValueCountFrequency (%)
1.71
 
0.1%
3.71
 
0.1%
5.91
 
0.1%
6.41
 
0.1%
7.62
0.3%
7.72
0.3%
8.12
0.3%
8.23
0.4%
8.31
 
0.1%
8.52
0.3%
ValueCountFrequency (%)
18.71
 
0.1%
18.11
 
0.1%
16.11
 
0.1%
162
 
0.3%
15.95
0.7%
15.83
0.4%
15.72
 
0.3%
15.62
 
0.3%
15.55
0.7%
15.46
0.9%

pctj_menores18
Real number (ℝ)

Missing 

Distinct287
Distinct (%)44.7%
Missing58
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean28.658879
Minimum4.7
Maximum99.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-11-10T22:42:58.760261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4.7
5-th percentile13.705
Q121.725
median29.45
Q335.075
95-th percentile41
Maximum99.7
Range95
Interquartile range (IQR)13.35

Descriptive statistics

Standard deviation9.5606394
Coefficient of variation (CV)0.33360131
Kurtosis5.2818605
Mean28.658879
Median Absolute Deviation (MAD)6.45
Skewness0.83877771
Sum18399
Variance91.405826
MonotonicityNot monotonic
2025-11-10T22:42:59.071375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
407
 
1.0%
29.57
 
1.0%
27.47
 
1.0%
36.37
 
1.0%
34.47
 
1.0%
23.46
 
0.9%
34.16
 
0.9%
33.86
 
0.9%
30.66
 
0.9%
33.36
 
0.9%
Other values (277)577
82.4%
(Missing)58
 
8.3%
ValueCountFrequency (%)
4.71
0.1%
5.71
0.1%
7.72
0.3%
8.71
0.1%
8.81
0.1%
9.51
0.1%
9.91
0.1%
101
0.1%
10.11
0.1%
10.22
0.3%
ValueCountFrequency (%)
99.71
0.1%
76.91
0.1%
67.51
0.1%
57.91
0.1%
56.21
0.1%
55.61
0.1%
54.52
0.3%
53.11
0.1%
481
0.1%
47.61
0.1%

pctj_hombres
Real number (ℝ)

High correlation  Missing 

Distinct133
Distinct (%)20.7%
Missing58
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean49.308879
Minimum27
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-11-10T22:42:59.280458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile44.8
Q147.8
median49.2
Q350.4
95-th percentile54
Maximum100
Range73
Interquartile range (IQR)2.6

Descriptive statistics

Standard deviation4.1331579
Coefficient of variation (CV)0.083821779
Kurtosis71.301169
Mean49.308879
Median Absolute Deviation (MAD)1.3
Skewness5.6285764
Sum31656.3
Variance17.082994
MonotonicityNot monotonic
2025-11-10T22:42:59.560459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.820
 
2.9%
49.119
 
2.7%
49.418
 
2.6%
48.817
 
2.4%
49.216
 
2.3%
49.916
 
2.3%
5015
 
2.1%
48.615
 
2.1%
50.115
 
2.1%
48.314
 
2.0%
Other values (123)477
68.1%
(Missing)58
 
8.3%
ValueCountFrequency (%)
271
0.1%
35.71
0.1%
39.31
0.1%
40.31
0.1%
40.92
0.3%
41.22
0.3%
41.51
0.1%
41.71
0.1%
41.81
0.1%
41.91
0.1%
ValueCountFrequency (%)
1002
0.3%
64.71
0.1%
63.61
0.1%
611
0.1%
601
0.1%
59.41
0.1%
591
0.1%
58.41
0.1%
57.51
0.1%
57.21
0.1%

pctj_mujeres
Real number (ℝ)

High correlation  Missing 

Distinct133
Distinct (%)20.7%
Missing58
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean50.691121
Minimum0
Maximum73
Zeros2
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-11-10T22:42:59.813958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile46
Q149.6
median50.8
Q352.2
95-th percentile55.2
Maximum73
Range73
Interquartile range (IQR)2.6

Descriptive statistics

Standard deviation4.1331579
Coefficient of variation (CV)0.081536131
Kurtosis71.301169
Mean50.691121
Median Absolute Deviation (MAD)1.3
Skewness-5.6285764
Sum32543.7
Variance17.082994
MonotonicityNot monotonic
2025-11-10T22:43:00.108003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.220
 
2.9%
50.919
 
2.7%
50.618
 
2.6%
51.217
 
2.4%
50.816
 
2.3%
50.116
 
2.3%
5015
 
2.1%
51.415
 
2.1%
49.915
 
2.1%
51.714
 
2.0%
Other values (123)477
68.1%
(Missing)58
 
8.3%
ValueCountFrequency (%)
02
0.3%
35.31
0.1%
36.41
0.1%
391
0.1%
401
0.1%
40.61
0.1%
411
0.1%
41.61
0.1%
42.51
0.1%
42.81
0.1%
ValueCountFrequency (%)
731
0.1%
64.31
0.1%
60.71
0.1%
59.71
0.1%
59.12
0.3%
58.82
0.3%
58.51
0.1%
58.31
0.1%
58.21
0.1%
58.11
0.1%

tasa_incidentes_per_1k
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct506
Distinct (%)78.4%
Missing55
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean7178.2889
Minimum0
Maximum3011666.7
Zeros140
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-11-10T22:43:00.378443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.2016807
median1036.8852
Q32820.5689
95-th percentile8487.2929
Maximum3011666.7
Range3011666.7
Interquartile range (IQR)2816.3672

Descriptive statistics

Standard deviation118639.32
Coefficient of variation (CV)16.52752
Kurtosis641.64225
Mean7178.2889
Median Absolute Deviation (MAD)1036.8852
Skewness25.298775
Sum4629996.4
Variance1.4075287 × 1010
MonotonicityNot monotonic
2025-11-10T22:43:00.569804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0140
 
20.0%
12.987012991
 
0.1%
4215.1368761
 
0.1%
2490.2102971
 
0.1%
3411.1498261
 
0.1%
2021.2765961
 
0.1%
365.85365851
 
0.1%
864.07766991
 
0.1%
4065.2680651
 
0.1%
1963.3867281
 
0.1%
Other values (496)496
70.9%
(Missing)55
 
7.9%
ValueCountFrequency (%)
0140
20.0%
0.29197080291
 
0.1%
0.48239266761
 
0.1%
0.50327126321
 
0.1%
0.58927519151
 
0.1%
0.79617834391
 
0.1%
0.80906148871
 
0.1%
0.92336103421
 
0.1%
11
 
0.1%
1.3793103451
 
0.1%
ValueCountFrequency (%)
3011666.6671
0.1%
77719.51221
0.1%
69136.363641
0.1%
54785.714291
0.1%
45813.559321
0.1%
35784.313731
0.1%
33200.714591
0.1%
26396.03961
0.1%
24715.217391
0.1%
22514.851491
0.1%

tasa_alta_severidad_per_1k
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct488
Distinct (%)75.7%
Missing55
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean2245.3418
Minimum0
Maximum936666.67
Zeros156
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-11-10T22:43:00.848415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.4184397
median328.03181
Q3977.67857
95-th percentile2873.4373
Maximum936666.67
Range936666.67
Interquartile range (IQR)976.26013

Descriptive statistics

Standard deviation36884.922
Coefficient of variation (CV)16.427308
Kurtosis642.54491
Mean2245.3418
Median Absolute Deviation (MAD)328.03181
Skewness25.324871
Sum1448245.5
Variance1.3604975 × 109
MonotonicityNot monotonic
2025-11-10T22:43:01.061843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0156
 
22.3%
461.53846152
 
0.3%
58.823529412
 
0.3%
9767.1052631
 
0.1%
602.98507461
 
0.1%
452.3396881
 
0.1%
1498.3713361
 
0.1%
1105.511241
 
0.1%
496.56750571
 
0.1%
566.5529011
 
0.1%
Other values (478)478
68.3%
(Missing)55
 
7.9%
ValueCountFrequency (%)
0156
22.3%
0.79617834391
 
0.1%
0.80906148871
 
0.1%
0.82576383151
 
0.1%
1.1786892981
 
0.1%
1.3486176671
 
0.1%
1.4184397161
 
0.1%
1.5455950541
 
0.1%
1.6155088851
 
0.1%
1.8382352941
 
0.1%
ValueCountFrequency (%)
936666.66671
0.1%
15431.818181
0.1%
15426.829271
0.1%
14571.428571
0.1%
10901.086961
0.1%
10423.728811
0.1%
9767.1052631
0.1%
9722.7826821
0.1%
9278.4810131
0.1%
80001
0.1%

score_severidad
Real number (ℝ)

High correlation  Zeros 

Distinct482
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5671598
Minimum0
Maximum3
Zeros175
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-11-10T22:43:01.474322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median2.023363
Q32.1326881
95-th percentile2.3724928
Maximum3
Range3
Interquartile range (IQR)1.3826881

Descriptive statistics

Standard deviation0.92724845
Coefficient of variation (CV)0.59167448
Kurtosis-0.74999921
Mean1.5671598
Median Absolute Deviation (MAD)0.13260861
Skewness-0.99723331
Sum1097.0118
Variance0.85978969
MonotonicityNot monotonic
2025-11-10T22:43:01.743747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0175
 
25.0%
218
 
2.6%
310
 
1.4%
16
 
0.9%
2.55
 
0.7%
2.1538461543
 
0.4%
2.6666666672
 
0.3%
2.1161616162
 
0.3%
2.752
 
0.3%
2.0526315792
 
0.3%
Other values (472)475
67.9%
ValueCountFrequency (%)
0175
25.0%
16
 
0.9%
1.51
 
0.1%
1.5652173911
 
0.1%
1.61
 
0.1%
1.6111111111
 
0.1%
1.6142857141
 
0.1%
1.6666666671
 
0.1%
1.6909589041
 
0.1%
1.7154195011
 
0.1%
ValueCountFrequency (%)
310
1.4%
2.81
 
0.1%
2.752
 
0.3%
2.6666666672
 
0.3%
2.5593220341
 
0.1%
2.5279503111
 
0.1%
2.55
0.7%
2.4421052631
 
0.1%
2.441
 
0.1%
2.4266666671
 
0.1%

area_km2
Real number (ℝ)

High correlation  Unique 

Distinct700
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24388744
Minimum0.0021870664
Maximum9.429745
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-11-10T22:43:02.056608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0021870664
5-th percentile0.011175935
Q10.042928439
median0.098850384
Q30.23107655
95-th percentile0.94420166
Maximum9.429745
Range9.4275579
Interquartile range (IQR)0.18814811

Descriptive statistics

Standard deviation0.55302747
Coefficient of variation (CV)2.2675521
Kurtosis121.3217
Mean0.24388744
Median Absolute Deviation (MAD)0.072333642
Skewness8.9982439
Sum170.72121
Variance0.30583938
MonotonicityNot monotonic
2025-11-10T22:43:02.329966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.037554739461
 
0.1%
0.15020732791
 
0.1%
0.046596943971
 
0.1%
0.13799809081
 
0.1%
0.0784228881
 
0.1%
0.037977750791
 
0.1%
0.094630328421
 
0.1%
0.18399345931
 
0.1%
0.064812267791
 
0.1%
0.018313395341
 
0.1%
Other values (690)690
98.6%
ValueCountFrequency (%)
0.0021870664451
0.1%
0.0023390559531
0.1%
0.0023940351261
0.1%
0.0024811588821
0.1%
0.0026185556871
0.1%
0.0027772979561
0.1%
0.0040881180141
0.1%
0.0043742538241
0.1%
0.004777987431
0.1%
0.0050312236061
0.1%
ValueCountFrequency (%)
9.429744971
0.1%
4.9086736461
0.1%
3.3631471761
0.1%
3.339431191
0.1%
3.3258383481
0.1%
2.9859444591
0.1%
2.585180271
0.1%
2.551009931
0.1%
2.2212831251
0.1%
2.2171812581
0.1%

densidad_poblacional
Real number (ℝ)

Missing 

Distinct645
Distinct (%)100.0%
Missing55
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean7710.8156
Minimum0.63628444
Maximum41766.406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-11-10T22:43:02.524825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.63628444
5-th percentile500.92614
Q13607.3042
median6945.9375
Q310931.787
95-th percentile16453.54
Maximum41766.406
Range41765.77
Interquartile range (IQR)7324.4825

Descriptive statistics

Standard deviation5347.765
Coefficient of variation (CV)0.69354077
Kurtosis3.3673217
Mean7710.8156
Median Absolute Deviation (MAD)3637.1913
Skewness1.1647288
Sum4973476
Variance28598590
MonotonicityNot monotonic
2025-11-10T22:43:02.677562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2050.3404131
 
0.1%
7320.1098841
 
0.1%
13714.357661
 
0.1%
11727.931181
 
0.1%
986.39772921
 
0.1%
12614.486721
 
0.1%
5529.8457011
 
0.1%
8282.9054661
 
0.1%
6230.3511241
 
0.1%
2303.6399681
 
0.1%
Other values (635)635
90.7%
(Missing)55
 
7.9%
ValueCountFrequency (%)
0.63628444031
0.1%
8.1314531221
0.1%
49.601430781
0.1%
53.520340251
0.1%
61.564956151
0.1%
105.53498521
0.1%
106.88010481
0.1%
111.57749281
0.1%
118.7051351
0.1%
129.8544551
0.1%
ValueCountFrequency (%)
41766.406121
0.1%
32472.717061
0.1%
32435.049031
0.1%
28477.634371
0.1%
24414.952241
0.1%
23939.348181
0.1%
23749.867671
0.1%
22360.962431
0.1%
21971.280751
0.1%
21145.61251
0.1%

Interactions

2025-11-10T22:42:38.045711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:41:50.116612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:41:53.634576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:41:56.942901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:42:00.094711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:42:03.614920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-11-10T22:42:24.440791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:42:27.650854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:42:31.035052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:42:34.189188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:42:37.691231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:42:40.860065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:41:53.428577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:41:56.752062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:41:59.906702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:42:03.444468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:42:06.843045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:42:10.457245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:42:13.512791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:42:16.440561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:42:20.504831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:42:24.646654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:42:27.838538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:42:31.248522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:42:34.412755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-10T22:42:37.886604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-11-10T22:43:02.870502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
CParea_km2densidad_poblacionalescolaridad_años_promincidentes_altaincidentes_bajaincidentes_mediapctj_hombrespctj_menores18pctj_mujerespoblacion_totalscore_severidadtasa_alta_severidad_per_1ktasa_incidentes_per_1ktotal_incidentesviviendas_totales
CP1.0000.3230.1640.0000.2740.2530.3740.0640.2930.0000.0000.0000.5940.5940.3260.000
area_km20.3231.000-0.335-0.2560.5770.5780.5850.117-0.031-0.1170.6750.3230.3810.3790.5850.684
densidad_poblacional0.164-0.3351.000-0.2270.0310.042-0.0110.0210.157-0.0210.346-0.063-0.125-0.1540.0130.316
escolaridad_años_prom0.000-0.256-0.2271.000-0.185-0.145-0.122-0.367-0.3180.367-0.362-0.173-0.115-0.052-0.147-0.327
incidentes_alta0.2740.5770.031-0.1851.0000.9870.9850.004-0.144-0.0040.5980.6480.9010.8800.9940.606
incidentes_baja0.2530.5780.042-0.1450.9871.0000.985-0.009-0.1560.0090.6070.5480.8780.8730.9920.615
incidentes_media0.3740.585-0.011-0.1220.9850.9851.000-0.032-0.2040.0320.5830.5850.8940.8950.9940.590
pctj_hombres0.0640.1170.021-0.3670.004-0.009-0.0321.0000.330-1.0000.0760.045-0.023-0.054-0.0110.062
pctj_menores180.293-0.0310.157-0.318-0.144-0.156-0.2040.3301.000-0.3300.0430.025-0.169-0.213-0.1690.024
pctj_mujeres0.000-0.117-0.0210.367-0.0040.0090.032-1.000-0.3301.000-0.076-0.0450.0230.0540.011-0.062
poblacion_total0.0000.6750.346-0.3620.5980.6070.5830.0760.043-0.0761.0000.2560.2890.2670.5970.981
score_severidad0.0000.323-0.063-0.1730.6480.5480.5850.0450.025-0.0450.2561.0000.6110.5520.6180.271
tasa_alta_severidad_per_1k0.5940.381-0.125-0.1150.9010.8780.894-0.023-0.1690.0230.2890.6111.0000.9870.8960.305
tasa_incidentes_per_1k0.5940.379-0.154-0.0520.8800.8730.895-0.054-0.2130.0540.2670.5520.9871.0000.8910.284
total_incidentes0.3260.5850.013-0.1470.9940.9920.994-0.011-0.1690.0110.5970.6180.8960.8911.0000.606
viviendas_totales0.0000.6840.316-0.3270.6060.6150.5900.0620.024-0.0620.9810.2710.3050.2840.6061.000

Missing values

2025-11-10T22:42:41.202778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-10T22:42:42.081134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-10T22:42:42.697900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

cve_entcve_muncve_locnom_locCVE_COLCOLONIACPotros_cptotal_incidentesincidentes_altaincidentes_mediaincidentes_bajacategorias_dictpartes_dia_dictincidentes_fin_semanaincidentes_quincenadias_semana_dictfecha_iniciofecha_finpoblacion_totalviviendas_totalesescolaridad_años_prompctj_menores18pctj_hombrespctj_mujerestasa_incidentes_per_1ktasa_alta_severidad_per_1kscore_severidadarea_km2densidad_poblacional
026301Hermosillo2603000016735ALTA FIRENZE NORTE RESIDENCIAL83104.0NaN1100{'DELITO PATRIMONIAL': 1}{'Tarde': 1}00{'Martes': 1}2018-06-19 17:00:002018-06-19 17:00:0077.068.014.928.649.450.612.98701312.9870133.0000000.0375552050.340413
126301Hermosillo2603000011785JORGE VALDEZ MUÑOZ83104.0NaN30661404870792{'CONVIVENCIA': 1009, 'VIOLENCIA': 799, 'EMERGENCIAS MÉDICAS': 406, 'DELITO PATRIMONIAL': 244, 'INCENDIOS Y EXPLOSIONES': 204, 'RESCATE': 125, 'TRÁNSITO': 108, 'INFRAESTRUCTURA': 53, 'OTROS ACTOS LEGALES': 50, 'DELITO CONTRA SALUD': 46, 'DESASTRES NATURALES': 19, 'ELECTORAL': 3}{'Noche': 989, 'Madrugada': 843, 'Tarde': 713, 'Mañana': 521}00{'Domingo': 750, 'Sábado': 472, 'Lunes': 463, 'Viernes': 371, 'Martes': 348, 'Miércoles': 337, 'Jueves': 325}2018-01-01 02:00:002025-09-30 04:00:001081.0366.09.435.749.950.12836.2627201298.7974102.1996090.1085859955.346547
226301Hermosillo2603000016335VILLA VERDE CERRADA SAN VICENTE83118.0NaN0000NaNNaN00NaNNaNNaN715.0261.09.540.452.447.60.0000000.0000000.0000000.06058411801.826946
326301Hermosillo2603000011480VILLA VENTURA83159.0NaN0000NaNNaN00NaNNaNNaN120.086.014.221.746.553.50.0000000.0000000.0000000.0176956781.506720
426301Hermosillo2603000011663NUEVO HERMOSILLO83296.0NaN43761154151392414422{'CONVIVENCIA': 18291, 'VIOLENCIA': 10773, 'DELITO PATRIMONIAL': 3245, 'EMERGENCIAS MÉDICAS': 2950, 'TRÁNSITO': 2711, 'INCENDIOS Y EXPLOSIONES': 1789, 'RESCATE': 1476, 'INFRAESTRUCTURA': 718, 'OTROS ACTOS LEGALES': 692, 'DELITO CONTRA SALUD': 555, 'DESASTRES NATURALES': 538, 'ELECTORAL': 23}{'Noche': 14404, 'Madrugada': 13146, 'Tarde': 9230, 'Mañana': 6981}00{'Domingo': 10522, 'Sábado': 7056, 'Lunes': 6029, 'Viernes': 5295, 'Jueves': 5050, 'Miércoles': 4966, 'Martes': 4843}2018-01-01 00:00:002025-09-30 23:00:0012629.04102.010.729.449.150.93465.1199621220.6033732.0226911.2667949969.263119
526301Hermosillo2603000011424SAN JERONIMO83125.0NaN1010{'DELITO PATRIMONIAL': 1}{'Mañana': 1}00{'Martes': 1}2025-05-13 07:00:002025-05-13 07:00:001083.0380.013.423.147.552.50.9233610.0000002.0000000.02593041766.406124
626301Hermosillo2603000011474MODELO83190.0NaN20260564992205391{'CONVIVENCIA': 5307, 'VIOLENCIA': 3892, 'TRÁNSITO': 3707, 'DELITO PATRIMONIAL': 1557, 'EMERGENCIAS MÉDICAS': 1530, 'RESCATE': 1403, 'DESASTRES NATURALES': 1046, 'INCENDIOS Y EXPLOSIONES': 644, 'INFRAESTRUCTURA': 542, 'OTROS ACTOS LEGALES': 448, 'DELITO CONTRA SALUD': 183, 'ELECTORAL': 1}{'Tarde': 5966, 'Noche': 5718, 'Mañana': 5244, 'Madrugada': 3332}00{'Viernes': 3005, 'Jueves': 2986, 'Lunes': 2968, 'Miércoles': 2893, 'Martes': 2890, 'Domingo': 2853, 'Sábado': 2665}2018-01-01 00:00:002025-09-30 20:00:003285.01444.013.914.646.353.76167.4277021719.6347032.0127341.1393282883.278754
726301Hermosillo2603000011521COLINA BLANCA83148.0NaN0000NaNNaN00NaNNaNNaN296.0101.014.919.347.053.00.0000000.0000000.0000000.01866015862.854101
826301Hermosillo2603000016740PRIVADAS DE MIRADOR II83106.0NaN1612569523520{'CONVIVENCIA': 658, 'VIOLENCIA': 417, 'EMERGENCIAS MÉDICAS': 122, 'DELITO PATRIMONIAL': 96, 'INCENDIOS Y EXPLOSIONES': 90, 'TRÁNSITO': 80, 'INFRAESTRUCTURA': 37, 'RESCATE': 35, 'OTROS ACTOS LEGALES': 31, 'DESASTRES NATURALES': 24, 'DELITO CONTRA SALUD': 22}{'Noche': 596, 'Madrugada': 458, 'Tarde': 329, 'Mañana': 229}00{'Domingo': 397, 'Sábado': 303, 'Lunes': 214, 'Viernes': 192, 'Jueves': 180, 'Miércoles': 164, 'Martes': 162}2018-05-14 01:00:002025-09-28 02:00:00563.0201.012.230.450.149.92863.2326821010.6571942.0303970.04211113369.321127
926301Hermosillo2603000011495VILLA GUADALUPE83105.0NaN1591453490648{'CONVIVENCIA': 765, 'VIOLENCIA': 286, 'TRÁNSITO': 130, 'DELITO PATRIMONIAL': 117, 'EMERGENCIAS MÉDICAS': 77, 'INCENDIOS Y EXPLOSIONES': 60, 'DESASTRES NATURALES': 46, 'INFRAESTRUCTURA': 42, 'RESCATE': 40, 'OTROS ACTOS LEGALES': 20, 'DELITO CONTRA SALUD': 8}{'Madrugada': 592, 'Noche': 464, 'Tarde': 302, 'Mañana': 233}00{'Domingo': 421, 'Sábado': 250, 'Lunes': 220, 'Miércoles': 182, 'Viernes': 178, 'Martes': 170, 'Jueves': 170}2018-01-01 05:00:002025-09-29 04:00:001141.0378.013.022.348.251.81394.390885397.0201581.8774360.08206413903.733156
cve_entcve_muncve_locnom_locCVE_COLCOLONIACPotros_cptotal_incidentesincidentes_altaincidentes_mediaincidentes_bajacategorias_dictpartes_dia_dictincidentes_fin_semanaincidentes_quincenadias_semana_dictfecha_iniciofecha_finpoblacion_totalviviendas_totalesescolaridad_años_prompctj_menores18pctj_hombrespctj_mujerestasa_incidentes_per_1ktasa_alta_severidad_per_1kscore_severidadarea_km2densidad_poblacional
69026301Hermosillo2603000011789PRIVADA LOS SAUCES83067.0NaN0000NaNNaN00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0000000.002777NaN
69126301Hermosillo2603000011801PRIVADA CASA BLANCA83079.0NaN0000NaNNaN00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0000000.002481NaN
69226301Hermosillo2603000011560PRADOS DEL SOL83100.0NaN1797549783465{'CONVIVENCIA': 670, 'VIOLENCIA': 279, 'TRÁNSITO': 200, 'EMERGENCIAS MÉDICAS': 172, 'DELITO PATRIMONIAL': 161, 'RESCATE': 94, 'INFRAESTRUCTURA': 82, 'DESASTRES NATURALES': 51, 'INCENDIOS Y EXPLOSIONES': 48, 'OTROS ACTOS LEGALES': 29, 'DELITO CONTRA SALUD': 11}{'Noche': 506, 'Tarde': 465, 'Madrugada': 449, 'Mañana': 377}00{'Sábado': 307, 'Domingo': 301, 'Jueves': 249, 'Viernes': 244, 'Miércoles': 238, 'Lunes': 238, 'Martes': 220}2018-01-01 07:00:002025-09-29 03:00:001080.0413.014.715.346.253.81663.888889508.3333332.0467450.1183599124.763089
69326301Hermosillo2603000011499SAN LUIS83160.0NaN17728787066513207{'VIOLENCIA': 4887, 'CONVIVENCIA': 4644, 'EMERGENCIAS MÉDICAS': 2518, 'RESCATE': 1335, 'DELITO PATRIMONIAL': 1334, 'TRÁNSITO': 971, 'INCENDIOS Y EXPLOSIONES': 944, 'INFRAESTRUCTURA': 407, 'DELITO CONTRA SALUD': 299, 'OTROS ACTOS LEGALES': 244, 'DESASTRES NATURALES': 133, 'ELECTORAL': 12}{'Noche': 5948, 'Tarde': 4615, 'Madrugada': 3782, 'Mañana': 3383}00{'Domingo': 3454, 'Sábado': 2622, 'Lunes': 2554, 'Viernes': 2333, 'Miércoles': 2292, 'Jueves': 2263, 'Martes': 2210}2018-01-01 00:00:002025-09-30 20:00:006239.01888.09.430.050.749.32841.4810071261.4200992.2630301.7819343501.251301
69426301Hermosillo2603000011686CERRO DE LA CAMPANA83000.0NaN51716825495{'CONVIVENCIA': 157, 'VIOLENCIA': 113, 'RESCATE': 49, 'TRÁNSITO': 47, 'DELITO PATRIMONIAL': 46, 'INCENDIOS Y EXPLOSIONES': 38, 'EMERGENCIAS MÉDICAS': 27, 'INFRAESTRUCTURA': 18, 'DESASTRES NATURALES': 10, 'OTROS ACTOS LEGALES': 9, 'DELITO CONTRA SALUD': 3}{'Noche': 167, 'Tarde': 137, 'Madrugada': 120, 'Mañana': 93}00{'Sábado': 89, 'Domingo': 87, 'Jueves': 79, 'Martes': 77, 'Viernes': 71, 'Lunes': 57, 'Miércoles': 57}2018-01-01 06:00:002025-09-25 02:00:00126.056.010.630.252.147.94103.1746031333.3333332.1411990.222699565.786509
69526301Hermosillo2603000011353EL ENCANTO83105.0NaN49142411{'RESCATE': 12, 'VIOLENCIA': 9, 'CONVIVENCIA': 8, 'TRÁNSITO': 8, 'EMERGENCIAS MÉDICAS': 6, 'DESASTRES NATURALES': 2, 'INFRAESTRUCTURA': 2, 'DELITO PATRIMONIAL': 1, 'INCENDIOS Y EXPLOSIONES': 1}{'Tarde': 16, 'Mañana': 12, 'Noche': 12, 'Madrugada': 9}00{'Martes': 12, 'Jueves': 9, 'Lunes': 8, 'Viernes': 6, 'Domingo': 5, 'Miércoles': 5, 'Sábado': 4}2019-04-13 11:00:002025-08-29 01:00:00896.0289.011.026.948.851.254.68750015.6250002.0612240.0992779025.237888
69626301Hermosillo2603000011416PRIMAVERA83113.0NaN4651625{'TRÁNSITO': 23, 'CONVIVENCIA': 11, 'VIOLENCIA': 5, 'DELITO PATRIMONIAL': 2, 'DESASTRES NATURALES': 2, 'EMERGENCIAS MÉDICAS': 1, 'INFRAESTRUCTURA': 1, 'RESCATE': 1}{'Mañana': 15, 'Noche': 13, 'Tarde': 10, 'Madrugada': 8}00{'Jueves': 9, 'Viernes': 8, 'Miércoles': 7, 'Sábado': 6, 'Domingo': 6, 'Lunes': 5, 'Martes': 5}2019-01-06 03:00:002023-06-24 18:00:00516.0170.010.223.851.948.189.1472879.6899221.5652170.0803396422.756658
69726301Hermosillo2603000011649COSTA DEL SOL83140.0NaN34891555940994{'CONVIVENCIA': 1165, 'VIOLENCIA': 942, 'EMERGENCIAS MÉDICAS': 371, 'TRÁNSITO': 291, 'DELITO PATRIMONIAL': 236, 'INCENDIOS Y EXPLOSIONES': 207, 'INFRAESTRUCTURA': 86, 'RESCATE': 78, 'OTROS ACTOS LEGALES': 47, 'DELITO CONTRA SALUD': 37, 'DESASTRES NATURALES': 28, 'ELECTORAL': 1}{'Noche': 1233, 'Madrugada': 1016, 'Tarde': 720, 'Mañana': 520}00{'Domingo': 827, 'Sábado': 592, 'Lunes': 468, 'Viernes': 423, 'Miércoles': 420, 'Jueves': 413, 'Martes': 346}2018-01-01 02:00:002025-09-30 17:00:001257.0436.011.229.149.051.02775.6563251237.0723952.1607910.6283552000.462425
69826301Hermosillo2603000011852VISTA DEL LAGO83240.0NaN3110165{'VIOLENCIA': 7, 'RESCATE': 6, 'EMERGENCIAS MÉDICAS': 5, 'CONVIVENCIA': 4, 'DELITO PATRIMONIAL': 3, 'OTROS ACTOS LEGALES': 3, 'INFRAESTRUCTURA': 2, 'INCENDIOS Y EXPLOSIONES': 1}{'Tarde': 10, 'Mañana': 9, 'Noche': 8, 'Madrugada': 4}00{'Jueves': 8, 'Viernes': 6, 'Sábado': 6, 'Lunes': 5, 'Domingo': 4, 'Martes': 2}2018-02-15 13:00:002025-06-22 10:00:0084.022.014.920.245.254.8369.047619119.0476192.1612900.0191804379.526851
69926301Hermosillo2603000011647CASA LINDA83140.0NaN1352536407409{'CONVIVENCIA': 564, 'VIOLENCIA': 310, 'DELITO PATRIMONIAL': 161, 'INCENDIOS Y EXPLOSIONES': 84, 'EMERGENCIAS MÉDICAS': 73, 'TRÁNSITO': 56, 'INFRAESTRUCTURA': 35, 'RESCATE': 26, 'OTROS ACTOS LEGALES': 16, 'DESASTRES NATURALES': 15, 'DELITO CONTRA SALUD': 12}{'Noche': 528, 'Madrugada': 358, 'Tarde': 291, 'Mañana': 175}00{'Domingo': 270, 'Sábado': 223, 'Lunes': 200, 'Viernes': 179, 'Jueves': 169, 'Martes': 165, 'Miércoles': 146}2018-01-01 05:00:002025-09-24 19:00:00731.0264.012.528.548.251.81849.521204733.2421342.0939350.07025410405.122916